AIMC Topic: Deep Learning

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Hello World Deep Learning in Medical Imaging.

Journal of digital imaging
There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availabi...

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

Current osteoporosis reports
PURPOSE OF REVIEW: To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine lea...

DeepSig: deep learning improves signal peptide detection in proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.

Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks.

IEEE transactions on medical imaging
Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge...

Deep learning of genomic variation and regulatory network data.

Human molecular genetics
The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleter...

Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
As one of the most common human helminths, hookworm is a leading cause of maternal and child morbidity, which seriously threatens human health. Recently, wireless capsule endoscopy (WCE) has been applied to automatic hookworm detection. Unfortunately...

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

Bioinformatics (Oxford, England)
MOTIVATION: Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networ...

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

Bioinformatics (Oxford, England)
MOTIVATION: While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a tim...

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.

Cell reports
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 ...